Ensemble of 2D Residual Neural Networks Integrated with Atrous Spatial Pyramid Pooling Module for Myocardium Segmentation of Left Ventricle Cardiac MRI

Ahmad, Iftikhar; Qayyum, Abdul; Gupta, Brij B.; Alassafi, Madini O.; AlGhamdi, Rayed

doi:10.3390/math10040627

Cited by 10 publications

(9 citation statements)

References 59 publications

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“…To reduce the time complexity, it is formulated as a three-stage operation using two (Convolution and ReLu) operations and a (Convolution, batch normalization, and ReLu) operation. It was evaluated and confirmed in some works [ 30 , 31 ]. Another well-known image denoising approach is autoencoder.…”

Section: Benchmark Datasets and Methodologymentioning

confidence: 75%

Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection

Chui¹,

Gupta

Ran

et al. 2022

Cancers

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Background: Prostate cancer is the 4th most common type of cancer. To reduce the workload of medical personnel in the medical diagnosis of prostate cancer and increase the diagnostic accuracy in noisy images, a deep learning model is desired for prostate cancer detection. Methods: A multi-scale denoising convolutional neural network (MSDCNN) model was designed for prostate cancer detection (PCD) that is capable of noise suppression in images. The model was further optimized by transfer learning, which contributes domain knowledge from the same domain (prostate cancer data) but heterogeneous datasets. Particularly, Gaussian noise was introduced in the source datasets before knowledge transfer to the target dataset. Results: Four benchmark datasets were chosen as representative prostate cancer datasets. Ablation study and performance comparison between the proposed work and existing works were performed. Our model improved the accuracy by more than 10% compared with the existing works. Ablation studies also showed average improvements in accuracy using denoising, multi-scale scheme, and transfer learning, by 2.80%, 3.30%, and 3.13%, respectively. Conclusions: The performance evaluation and comparison of the proposed model confirm the importance and benefits of image noise suppression and transfer of knowledge from heterogeneous datasets of the same domain.

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Section: Benchmark Datasets and Methodologymentioning

confidence: 75%

Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection

Chui¹,

Gupta

Ran

et al. 2022

Cancers

Self Cite

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“…Every crop is grown differently [8]. For fruit crops, k-means agglomeration is the segmentation method employed, with texture options focusing on ANN [9] and closest neighbour algorithms to achieve an overall average accuracy of 90.6%. For vegetable crops, chan-vase segmentation, native binary patterns for texture feature extraction, SVM, and closest neighbour classification achieved an overall average accuracy of 87.9%.…”

Section: Related Workmentioning

confidence: 99%

Plant Disease Detection using Image Processing

Mishra

Chaurasia²,

Arya³

et al. 2023

Lecture Notes in Networks and Systems

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Plant diseases have an impact on the development of their particular species, hence early detection is crucial. Numerous Machine Learning (ML) models have been used for the identification and classification of plant diseases, this field of study now appears to have significant potential for improved accuracy. In order to identify and categorise the signs of plant diseases, numerous developed/modified ML architectures are used in conjunction with a number of visualisation techniques. Additionally, a number of performance indicators are employed to assess these structures and methodologies.

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“…Recently, Syn-ISS (synthetic data for instrument segmentation in surgery) challenge using synthetic datasets is organized to develop high performance methods for instrument segmentation [3]. Recently, there is a different method has been proposed using medical imaging and signals [4], [5], [6], [7], [8] for classification and segmentation. Based on our previous work on segmentation [6], [7], [8], [9] Compare performance on synthetic data for instrument segmentation in surgery for binary and multiclass surgery instrument segmentation.…”

Section: Introductionmentioning

confidence: 99%

Parallel Cross Window Attention Transformer and CNN Model for Segmentation of Instrument during Surgery

Qayyum,

Niederer,

Khan

et al. 2024

Proceedings of International Conference on Artificial Life and Robotics

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Precise segmentation of surgical instruments is a fundamental component in the development of computer-aided surgery systems by assisting the surgeons to navigate the patient's body aiming to enhance the surgical precision and patient safety. Though real-time tracking of surgical instruments is critically important in invasive computer-assisted surgeries, it is challenging to achieve a highly sensitive and accurate system in complex surgical environment. Recently, synthetic data for instrument segmentation in surgery (Syn-ISS) challenge using synthetic datasets is organized to develop high performance methods for instrument segmentation. In this work, we present encoder and decoder-based hybrid parallel cross window attention-based transformer during the feature extraction, which consists of the multi-scale channel attention, convolutional layers, and Transformer layers, forming a unified block. Syn-ISS challenge dataset comprised of two tasks. In first task1, they need to develop deep learning-based method for binary instrument segmentation and in second task multiclass instrument segmentation is required. Experiments conducted on Syn-ISS dataset achieved 0.993 F-score for task 1 and 0.993, 0.975, and 0.951 F-score for shaft, wrist, and jaw segmentation respectively for Task 2.

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Ensemble of 2D Residual Neural Networks Integrated with Atrous Spatial Pyramid Pooling Module for Myocardium Segmentation of Left Ventricle Cardiac MRI

Cited by 10 publications

References 59 publications

Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection

Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection

Plant Disease Detection using Image Processing

Parallel Cross Window Attention Transformer and CNN Model for Segmentation of Instrument during Surgery

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